Hotel AI Adoption Is Failing on People, Not Technology

More than half of AI pricing recommendations get overridden industry-wide — not because the algorithms are wrong, LodgIQ’s Mark Charlinski argues in a new opinion piece, but because most hotels skip the trust-building stages staff need before they’ll accept full automation, jumping straight from manual pricing to expecting blind trust in a black box. A supporting number: 42% of hoteliers say their own employees experience workplace technology as a source of friction rather than help, which Charlinski attributes largely to rushed training and interfaces built with more complexity than the day-to-day role actually requires.

His recommended sequence is explicitly staged, not a single rollout event: start with AI suggestions under human oversight, move to guardrail-based automation where the system acts within defined limits, and only then progress to strategic monitoring where humans supervise at a higher level instead of approving every action. He also recommends small internal hackathons before a property-wide launch, specifically to surface champions who can advocate for the tool among peers rather than have it handed down from corporate.

The data backs up how wide this human gap actually is against how far the technology has already gotten. Mews’ March 2026 survey of more than 500 hoteliers found 98% already use AI in at least one operational area, spanning an average of 11 of 19 tracked tasks — roughly 56% of total workload — with 92% of respondents optimistic about AI’s role in hospitality generally. Yet Mews also names data accuracy concerns, privacy worries, and fragmented technology stacks as the barriers actually blocking further adoption, and one cited expert prediction puts hybrid AI-human service models as standard by 2035, not sooner. The technology question, as Charlinski frames it, is largely solved. The organizational one — whether a property has given itself the time and structure to actually use what it bought — is where the real adoption gap sits, and it’s a structural, staffing-level problem before it’s a software one.